Snow specific surface area remote sensing retrieval using a microstructure based reflectance model

The snow specific area (SSA) is a quantity to describe the size of snow grains, which controls the albedo of snow surface. The remote sensing retrieval of snow SSA is important for large scale energy and snow physical properties studies. The previous SSA retrieval algorithms are based on snow reflectance forward models with shape assumptions of snow grains, which may cause bias in the quantitative retrieval of SSA. In this paper, a new SSA retrieval algorithm based on a recently developed snow reflectance model is presented. In the reflectance model, the microstructure of snow medium is simulated by computer and the light scattering process is modeled by Monte Carlo ray tracing and radiative transfer theory. Validation shows the model can accurately simulate snow surface reflectance even at large incident or observation zenith angles. Three datasets were used to validate the SSA retrieval algorithm. The validation results show that the SSA can be accurately estimated using MODIS data. The difference of SSA by using reflectance of different wavelengths are studied by MODIS band 5 and band 6 data. The SSA retrieval algorithm is then applied to seasonal snow cover to study the temporal and spatial pattern of snow surface optical grain size.